Data is the fuel that drives your business growth. In the digital race in the Nordic region, a strong and scalable data foundation is crucial. It unlocks efficiency, drives innovation, and provides a competitive advantage. Poor data quality or systems that can't scale can slow progress. This is especially true if you want to fully leverage generative AI (GenAI) technologies.
Based on my work with various companies over the past 15 years, in this article, I will explain:
- Why data knowledge is important.
- How to create a strong data foundation.
- What you should do to get started.
Why data quality fuels growth
High-quality data, i.e., data that is accurate, complete, and up-to-date, is the backbone of digital transformation. Low-quality data causes wrong analyses and bad reports. This unreliable information leads to poor decisions and can result in fines. McKinsey’s 2025 report states that companies with strong data management can reduce the cost of decision-making by up to 30% (McKinsey, 2025). High data quality helps a company excel in its operations, as good data can be used to automate work processes, obtain accurate analyses as a basis for decisions, and report correctly and in a timely manner. All of which contribute to fueling the growth in a company.
Building a scalable data foundation
A scalable data foundation relies on modern ideas like Data Mesh and Data Lakehouse. These concepts focus on flexibility, quality, and compliance. Data Mesh shifts ownership. It allows teams in e.g. finance, production, and marketing to view data as a product. This includes clear documentation and quality guarantees. Data Lakehouse mixes the flexibility of data lakes with the structure of warehouses. It uses open formats like Apache Iceberg for schema evolution and scalability. Inside your Data Mesh or Data Lakehouse, you can use a data architecture like the Medallion Architecture. The layered method improves scalability. The medallion architecture uses Bronze (raw data), Silver (cleaned data), and Gold (curated data) to the different data layers. This helps to keep data in high-quality for e.g. analytics and GenAI applications.
The best practice from an infrastructure perspective is to separate compute from storage. For example, use Databricks for processing and Apache Parquet on Amazon S3 for storage. This helps decouple components and improve performance. Also, embrace open standards and open source to prevent vendor lock-in. To make the underlying solution scale, automation is critical. Infrastructure as Code (IaC) and CI/CD pipelines make data workflows smoother and cut down on errors. Tools like the DataHub data catalog help with governance and keep things GDPR compliant.
Finally, standards like ISO 8000 focus on data provenance and portability. This makes data trustworthy and ensures it meets GDPR’s accuracy principle (Article 5(1)(d)) (ISO, 2023).
These principles help Nordic firms manage increasing data volumes. They also ensure compliance with regulations and boost GenAI-driven efficiency.
Your next move
To build a scalable data foundation, you need to start with a strategic vision. If you rush this phase, you risk making costly mistakes. First, define your business goals. Do you want to personalise customer experiences, optimise supply chains or improve reporting? Set quantifiable goals, such as reducing customer churn by 10% or reducing reporting time from weeks to days. Next, review your data landscape: map silos, assess quality and create catalogues of tools and team competencies. Finally, develop a roadmap that starts with a minimum viable product and rolls out gradually. And remember to ensure that GDPR compliance is built in from the start, especially if you want to use GenAI. With the EU's new AI Act, breaches of data regulations will result in fines of up to €35 million (European Commission, 2024).
– A solid strategic foundation ensures that your data drives growth, not delays!
References:
ISO. (2023). ISO 8000: Data Quality and Master Data. Link
European Commission. (2024). The EU AI Act: Regulatory Framework. Link
McKinsey & Company. (2025). The State of AI: March 2025. Link
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